6 papers
OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agents
Yulin Hu, Zimo Long, Jiahe Guo +5
Memory-augmented conversational agents enable personalized interactions using long-term user memory and have gained substantial traction. However, existing benchmarks primarily foc…
STAR-S: Improving Safety Alignment through Self-Taught Reasoning on Safety Rules
Di Wu, Yanyan Zhao, Xin Lu +2
Defending against jailbreak attacks is crucial for the safe deployment of Large Language Models (LLMs). Recent research has attempted to improve safety by training models to reason…
CARE-Bench: A Benchmark of Diverse Client Simulations Guided by Expert Principles for Evaluating LLMs in Psychological Counseling
Bichen Wang, Yixin Sun, Junzhe Wang +6
The mismatch between the growing demand for psychological counseling and the limited availability of services has motivated research into the application of Large Language Models (…
Psychological Counseling Cannot Be Achieved Overnight: Automated Psychological Counseling Through Multi-Session Conversations
Junzhe Wang, Bichen Wang, Xing Fu +3
In recent years, Large Language Models (LLMs) have made significant progress in automated psychological counseling. However, current research focuses on single-session counseling,…
How Does Sequence Modeling Architecture Influence Base Capabilities of Pre-trained Language Models? Exploring Key Architecture Design Principles to Avoid Base Capabilities Degradation
Xin Lu, Yanyan Zhao, Si Wei +3
Pre-trained language models represented by the Transformer have been proven to possess strong base capabilities, and the representative self-attention mechanism in the Transformer…
Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety Re-Alignment for Fine-Tuned Language Models
Di Wu, Xin Lu, Yanyan Zhao +1
Although large language models (LLMs) achieve effective safety alignment at the time of release, they still face various safety challenges. A key issue is that fine-tuning often co…